One challenge organizations have today is the lack of data to validate bold moves, like strategic decisions to change policies, procedures, and products. Journey analytics allows you to take advantage of quantitative and qualitative insights gathered from across the business and infuse them into a journey-based view for more credible, data-driven decision-making.
Journey analytics includes capturing customer feedback from everywhere, pairing this data with customer insights like behavior and demographic information, assessing the meaning behind these insights in the frame of the customer journey and sharing these facts broadly across the company to drive the changes necessary to reduce friction and make it easier to do business with your company.
“Journey analytics combines big data technology, advanced analytics, and functional expertise to help companies perfect their customer journeys. To map them, it leverages millions of data points across customers, channels, and touchpoints” – McKinsey
Here is a high-level guide to embarking on journey analytics. For a more in-depth understanding of each of these steps, check out our eBook 5 Steps to Improving the Customer Journey with Analytics
1- Gather the data
Customers leave clues peppered throughout every interaction they have with your brand. When you bring the data together, from across the contact center, emails, social, chat or any other feedback channel, it enables you to visualize the journey, drill into friction points and uncover sentiment. Remember that the little details matter. Data accumulates in many areas, and when viewed together, often provide a different perspective.
2- Reshape customer feedback
Remember that the data is both quantitative and qualitative. In order to pair structured, quantitative data like the amount of last purchase, age, location and products used, with qualitative feedback data, you’ll need to use Natural Language Processing (NLP) to turn the unstructured voice of the customer into something that can be analyzed.
With this, you’ll be able to uncover customer emotions and identify potential friction points based on how the customer talks about the experience. Discover related topics, like how often customers talk about one topic in relation to another. Sentiment analysis can determine what customers like or what they have an aversion to and why. Bringing these together will help you identify your “moments of truth”.
3- Analyze Customer Data in a High-Level Journey
Now that you’ve successfully used a tool to transform multiple sources of customer feedback into a usable dataset, you should start at the highest level of the journey and place the data into logical journey touchpoints.
For example, a bank might segment the data for the “Open an Account” journey like this: Research Checking Accounts ➔ Open Account ➔ Deposit Initial Funds ➔ Establish Online Connectivity.
4- Take action
This may be the most important step in a journey analytics project. To make journey improvements happen, you have to show people a path to change and a reason for doing it. Therefore, you should socialize your findings with key stakeholders across your entire organization through interactive dashboards that are relevant to their specific view of the business.
5- Avoid pitfalls
According to Forrester, companies slip into an impoverished use of journey analytics when they use it in one of three ways: to validate assumptions instead of to discover; in a silo, instead of throughout the enterprise; and as a one-off project instead of a change management tool.
To avoid this, use analytics to inform your journeys with both qualitative and quantitative insights, bring in feedback from multiple sources and present it based on how the customer interacts with you. Most importantly, share it on a regular basis with your key stakeholders via dynamic dashboards.
Journey analytics are easy to implement with the right tools. There are many benefits of using journey analytics as your primary customer experience view, mainly being a customer-first organization. When undertaking a journey analytics project, be sure to map customer statistical data (demographic and behavioral) with the customer voice, their feedback and friction points. When you do this, you’ll improve the parts of your business that matter most to your customers.
For a more in-depth understanding of each of these steps, check out our eBook 5 Steps to Improving the Customer Journey with Analytics